cadCAD builder docs

docs/FAQ.md

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 The following are frequently asked questions with regards to MSML.

docs/cadCADBuilder.md

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+---
+title: cadCAD Builder
+layout: page
+nav_order: 5
+---
+
+MSML provides interfaces for creating cadCAD style models that don't require end users poking around the actual code to use. The idea is that it provides a layer for data scientists to experiment just with toggling starting state and parameters and executing pre-packaged models.
+
+## Components
+
+### Inputs
+
+The following is required for building the cadCAD model:
+
+1. A math spec object with code bindings
+2. A list of blocks which should be executed for each timestep
+3. What state preparation and parameter preparation functions should be included to automatically run on experiment creation
+
+
+### Outputs
+
+The following is outputted by the creation function:
+
+1. State Space: The nested and typed dictionary of all types for the state in the simulation that the data scientist needs to define
+2. Parameter Space: The dictionary of parameters that the data scientist needs to define
+3. Model: The executable model that can spawn experiments to run given valid state space and parameter space passed
+
+## Creating a cadCAD Model in a Notebook
+
+The [template notebook on building cadCAD models](https://github.com/BlockScience/MSML-Template/blob/main/notebooks/Build%20cadCAD.ipynb) shows how to create a cadCAD model from an MSML model.
+
+## Using cadCAD Model Repositories
+
+A recommended design pattern is to have MSML developers create a batch of cadCAD models to be run within a cadCADModels folder [similar to the template](https://github.com/BlockScience/MSML-Template/tree/main/cadCADModels).
+
+The template also has an example of running the pre-built cadCAD models [here](https://github.com/BlockScience/MSML-Template/blob/main/notebooks/Pre-built%20cadCAD.ipynb).
+
+
+## Model Functionality
+
+### Experiment Creation
+
+- To create an experiment, one runs model.create_experiment(state, params) and passes in a valid state and parameter dictionary
+- The record_trajectory flag if set to true will record the state after each step of the simulation
+- The use_deepcopy flag if set to true will use deepcopy for the trajectory recording
+
+
+### Running Experiments
+
+- Using experiment.run(T) where T is the number of time steps will advance the simulations that many timesteps
+- If record_trajectory was set to true then the trajectories can be accessed from experiment.trajectories, if not you can always access the current state of the experiment by calling experiment.state
+
+### Batch Experiments
+
+- Batch experiments can be created and run through model.create_batch_experiments(...). An example of it is shown in the pre-built cadCAD notebook.